Mallesham Dasari

I am currently a Postdoctoral Researcher at Carnegie Mellon University, advised by Prof. Anthony Rowe and Prof. Srinivasan Seshan. My research spans across Multimedia Systems, Mobile Computing, and Wireless Networking, with a current focus on improving the experience of Internet applications (e.g., On-Demand Video Streaming, 360-Degree and Volumetric Video Streaming, Augmented and Virtual Reality, etc).

Before CMU, I received a PhD in Computer Science from Stony Brook University in 2021 under the guidance of Prof. Samir Das. During my PhD, I also closely worked with Prof. Aruna Balasubramanian. Before Stony Brook, I was a Linux Kernel Developer at UURMI Systems (a startup acquired by Mathworks Inc. in 2016). Before that, I received my M.Tech and B.E from OSMANIA University, Hyderabad, India.

Email is the best way to contact me:


Swift: Adaptive Video Streaming with Layered Neural Codecs
Mallesham Dasari, Kumara Kahatapitiya, Samir R. Das, Aruna Balasubramanian, Dimitris Samaras
NSDI 2022 (Conference on Networked Systems Design and Implementation)

L3BOU: Low Latency, Low Bandwidth, Optimized Super-Resolution Backhaul for 360-Degree Video Streaming
Ayush Kumar, John Murray, Mallesham Dasari, Michael Zink, Klara Nahrstedt
ISM 2021 (Conference on Multimedia)
Paper Slides Best Paper Award

dcSR: Practical Video Quality Enhancement Using Data-Centric Super Resolution
Duin Baek, Mallesham Dasari, Jihoon Ryoo, Samir R. Das
CoNEXT 2021 (Conference on Emerging Networking Experiments and Technologies)
Paper Slides Code

Streaming 360-Degree Videos Using Super-Resolution
Mallesham Dasari, Arani Bhattacharya, Santiago Vargas, Pranjal Sahu, Aruna Balasubramanian, Samir R. Das
INFOCOM 2020 (Conference on Computer Communications)
Paper Slides Code Video

Advancing User Quality of Experience in 360-Degree Video Streaming
Sohee Park, Arani Bhattacharya, Zhibo Yang, Mallesham Dasari, Samir R. Das, Dimitris Samaras
Networking 2019 (Conference on Networking)

Spectrum Protection from Micro-Transmissions using Distributed Spectrum Patrolling
Mallesham Dasari, Muhammad Bershgal Atigue, Arani Bhattacharya, Samir R. Das
PAM 2019 (Conference on Passive and Active Network Measurements)
Paper Slides

Impact of Device Performance on Mobile Internet QoE
Mallesham Dasari, Santiago Vargas, Arani Bhattacharya, Aruna Balasubramanian, Samir R. Das, and Michael Ferdman
IMC 2018 (Conference on Internet Measurements)
Paper Slides Data

Scalable Ground-Truth Annotation for Video QoE Modeling in Enterprise WiFi
Mallesham Dasari, Christina Vlachou, Shruti Sanadhya, Kyu-Han Kim, Samir R. Das
IWQoS 2018 (Conference on Quality of Service)
Paper Technical Report

Active Research Projects

Neural Video Compression and Streaming

Video compression plays a central role for Internet video applications in reducing the network bandwidth requirement. Traditional algorithm-driven compression methods have served well to realize today's Internet video applications with an acceptable user experience. However, emerging 4K/8K/360-Degree video streaming, and AR/VR applications require orders of magnitude more bandwidth than today's applications. The monolithic, application-unware nature of the current generation compression algorithms is not scalable to realize such nearfuture applications over the Internet. This project explores data-driven techniques to significantly change the landscape of the source compression algorithms and improve the experience of next-generation video applications.

Multi-User Tracking for AR/VR Applications

The interactive and immersive applications such as Augmented Reality (AR) and Virtual Reality (VR) have significant potential for various tasks like industrial training, collaborative robotics, remote operation, etc. A key challenge to deliver these applications is to provide accurate and robust tracking of multiple agents (humans and robots) involved in every-day, challenging environments. Current AR/VR solutions rely on visual tracking algorithms (e.g., SLAM/Odometry) that are highly sensitive to environment (e.g., lighting conditions). This project explores augmenting the RF-positioning (e.g., WiFi/UWB) to improve the tracking in terms of accuracy, robustness, and scalability across multiple agents.

Teaching Experience

CSE 570: Wireless and Mobile Networks, Spring 2020

This class is about fundamental principles of wireless and mobile networking. Some of the topics that we will cover are the following:

  • Wireless Signals, Properties, Protocols
  • Wireless Physical Layer
  • Spectrum Sharing
  • RF-based Localization
  • Wireless Link Layer
  • Mobile IP
  • Wireless Transport
  • Mobile and Wireles Applications
  • Mobile Web
  • Mobile Video
  • RF Sensing
  • Mobile Devices, Performance and Energy Management
  • Deep Learning in Mobile and Wireless Applications

Academic Service


  • ACM MMSys, Program Committee


  • ACM Student Workshop at MobiSys, Program Co-Chair
  • ACM IMWUT/UbiComp Reviewer
  • ACM MM, Program Committee
  • ACM NOSSDAV, Program Committee


  • IEEE Pervasive Computing, Reviewer
  • ACM SIGCOMM, Artifact Evaluation Committee (AEC)


  • ACM S3 Workshop at MobiCom, Program Co-Chair
  • ACM Tansactions on Sensor Networks, Reviewer